Article
Version 1
Preserved in Portico This version is not peer-reviewed
Dynamic architecture adaptation for MRI brain tumor Detection using SECNNs
Version 1
: Received: 15 June 2024 / Approved: 17 June 2024 / Online: 17 June 2024 (11:27:33 CEST)
How to cite: beyaye, C. A. E.; Keradi, I. Dynamic architecture adaptation for MRI brain tumor Detection using SECNNs. Preprints 2024, 2024061144. https://doi.org/10.20944/preprints202406.1144.v1 beyaye, C. A. E.; Keradi, I. Dynamic architecture adaptation for MRI brain tumor Detection using SECNNs. Preprints 2024, 2024061144. https://doi.org/10.20944/preprints202406.1144.v1
Abstract
This paper presents an advanced approach for the Detection of MRI brain tumor images using Convolutional Neural Networks (CNNs). The study focuses on employing sophisticated techniques such as data augmentation, model optimization, and rigorous performance evaluation metrics to achieve high accuracy in multi-class classification tasks. Additionally, we integrate the concept of Self-Expanding Convolutional Neural Networks (SECNNs), which dynamically adjust model complexity during training. This approach ensures optimal performance while maintaining computational efficiency. The dataset used in this research comprises MRI images categorized into four distinct classes: glioma, meningioma, no tumor, and pituitary tumor. Our method demonstrates a notable test accuracy of 93.98%
Keywords
CNN
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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